Proceedings ArticleDOI
Expression Classification in Children Using Mean Supervised Deep Boltzmann Machine
Shruti Nagpal,Maneet Singh,Mayank Vatsa,Richa Singh,Afzel Noore +4 more
- pp 236-245
TLDR
This is the first research which presents a deep learning based expression classification approach for children and a novel supervised deep learning formulation, termed as Mean Supervised Deep Boltzmann Machine (msDBM) is proposed which classifies an input face image into one of the seven expression classes.Abstract:
Automated facial expression classification has widespread application in multiple domains such as human computer interaction, health and entertainment, biometrics, and security. There are six basic facial expressions: Anger, Disgust, Fear, Happiness, Sadness, and Surprise, apart from a neutral state. Most of the research in expression classification has focused on adult face images, with no dedicated research on automating expression classification for children. To the best of our knowledge, this is the first research which presents a deep learning based expression classification approach for children. A novel supervised deep learning formulation, termed as Mean Supervised Deep Boltzmann Machine (msDBM) is proposed which classifies an input face image into one of the seven expression classes. The proposed approach has been evaluated on two child face datasets - Radboud Faces and CAFE, along with experiments on the adult face images of the Radboud Faces dataset. Experimental results and analysis reinforces the challenging nature of the task at hand, and the effectiveness of the proposed msDBM model.read more
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Posted Content
Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders.
Peter Washington,Haik Kalantarian,Jack Kent,Arman Husic,Aaron Kline,Emilie Leblanc,Cathy Hou,Cezmi Mutlu,Kaitlyn Dunlap,Yordan Penev,Maya Varma,Nate Tyler Stockham,Brianna Chrisman,Kelley Paskov,Min Woo Sun,Jae-Yoon Jung,Catalin Voss,Nick Haber,Dennis P. Wall +18 more
TL;DR: This work validates that mobile games designed for pediatric therapies can generate high volumes of domain-relevant datasets to train state of the art classifiers to perform tasks highly relevant to precision health efforts.
Proceedings ArticleDOI
Transfer learning approach to multiclass classification of child facial expressions
TL;DR: This work proposes a transfer learning approach for multi-class classification of the seven prototypical expressions including the ‘neutral’ expression in children using a recently published child facial expression data set and holds promise to facilitate the development of technologies that focus on children and monitoring of children throughout their developmental stages to detect early symptoms related to developmental disorders.
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Cross-dataset emotion recognition from facial expressions through convolutional neural networks
TL;DR: In this article , a cross-dataset evaluation protocol was adopted to assess the performance of the proposed methods, which achieved state-of-the-art results, outperforming even commercial off-the -shelf solutions from well-known tech companies.
Journal ArticleDOI
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study
TL;DR: In this paper , the authors used GuessWhat, a therapeutic smartphone game designed for children with developmental and behavioral conditions, to gamify the secure collection of video data of children expressing a variety of emotions prompted by the game.
Proceedings ArticleDOI
Learning latent expression labels of child facial expression images through data-limited domain adaptation and transfer learning
TL;DR: This work combines deep transfer learning and domain adaptation approaches to generate seven expression labels for facial images of children in reference to the source domain, adult facial expressions, using 10 or fewer samples per expression.
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